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Locally linear embedding is a kind of very competitive nonlinear dimensionality reduction with good representational capacity for a broader range of manifolds and high computational efficiency. However, they are based on the assumption that the whole data manifolds are evenly distributed so that they determine the neighborhood for all points with the same neighborhood size. Accordingly, they fail to nicely deal with most real problems that are unevenly distributed. This paper presents a newdoi:10.3724/sp.j.1001.2008.01666 fatcat:3ykv5fpmwjgpzlq7bh6dem2m6q